Evidence map›Paper›PMID 41560366›Full record

ArticleInfection control and hospital epidemiology2026

A public health risk model using prior healthcare exposures identifies healthcare-associated pathogen carriage.

Sarah E Sansom, Tanner Shull, Mary K Hayden, Michael Schoeny, Angela Tang, Mai Vue, Anh-Thu Runez, Dejan Jovanov, William E Trick, Michael Y Lin

Abstract read
In one paragraph

Article in Infection control and hospital epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Sarah E SansomDepartment of Internal Medicine, Rush Universityhttps://ror.org/01j7c0b24, Chicago, IL, USA.ORCID https://orcid.org/0000-0002-1924-8941
Tanner ShullDepartment of Internal Medicine, Rush Universityhttps://ror.org/01j7c0b24, Chicago, IL, USA.ORCID https://orcid.org/0009-0003-9282-4874
Mary K HaydenDepartment of Internal Medicine, Rush Universityhttps://ror.org/01j7c0b24, Chicago, IL, USA.ORCID https://orcid.org/0000-0002-4603-8501
Michael SchoenyCollege of Nursing, Rush University, Chicago, IL, USA.
Angela TangIllinois Department of Public Health, Chicago, IL, USA.ORCID https://orcid.org/0009-0001-9206-9579
Mai VueIllinois Department of Public Health, Chicago, IL, USA.
Anh-Thu RunezIllinois Department of Public Health, Chicago, IL, USA.
Dejan JovanovIllinois Department of Public Health, Chicago, IL, USA.
William E TrickDepartment of Internal Medicine, Rush Universityhttps://ror.org/01j7c0b24, Chicago, IL, USA.
Michael Y LinDepartment of Internal Medicine, Rush Universityhttps://ror.org/01j7c0b24, Chicago, IL, USA.ORCID https://orcid.org/0000-0003-2992-6221

Funding

Institutional Career Development Core (Chapter 1)KL2TR002387 · NCATS · UNIVERSITY OF CHICAGO · PI ERIC C BEYER, Lisa L Barnes · 2017 to 2026
$7.6M
NCATS NIH HHS KL2 TR002387
6 · The paper itself

Abstract

backgroundEarly identification of patients colonized with multidrug-resistant organisms (MDROs) facilitates infection control interventions. We assessed a Public Health Risk Model's ability to predict carbapenem-resistant Enterobacterales and other MDROs.

methodsWe retrospectively analyzed a medical intensive care unit patient cohort screened at time of admission for MDRO carriage (1/2017-1/2018). Encounters were linked to Illinois Hospital Discharge Data and assigned a public health risk model probability score. We compared the model's performance to traditional screening strategies that use variables locally available to clinicians at time of admission (i.e., transfer from other hospital, tracheostomy, gastrostomy, pressure ulcer). Model discrimination was evaluated by quantifying the area under the curve (AUC). For each approach, we assessed sensitivity, specificity, and number needed to screen (NNS) to detect one MDRO carrier.

resultsModel probability calculation was successful in 1237/1250 (98.9%) admissions. The model identified carbapenem-resistant Enterobacterales colonization well (AUC 0.82) and generalized to predict colonization with other healthcare-associated MDROs, including carbapenem-resistant

conclusionA risk model using patient-level healthcare exposure data from a state public health dataset identified critically ill patients likely to harbor healthcare-associated MDROs at the time of admission.

Indexed as

Carrier StateCross InfectionAgedCarbapenem-Resistant EnterobacteriaceaeDrug Resistance, Multiple, BacterialFemaleHumansIllinoisIntensive Care UnitsMaleMethicillin-Resistant Staphylococcus aureusMiddle AgedPublic HealthRetrospective StudiesRisk AssessmentVancomycin-Resistant Enterococci

Identifiers

PMID41560366
PMCPMC12885053

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.